Human-computer interactions have been primarily detected using direct manipulation of devices such as mice, keyboards, pens, dials, and touch-sensitive surfaces. As computing and digital information becomes integrated into everyday environments, situations arise where it may be inconvenient or difficult to use hands to directly manipulate an input device. For example, a driver attempting to query a vehicle navigation system might find it helpful to be able to do so without removing his or her hands from the steering wheel. A person in a meeting may wish to unobtrusively interact with a computing device. Furthermore, direct physical manipulation spreads microbes, so hands-free input mechanisms can helpful in areas sensitive to contamination, for example surgery rooms.
Most implement-free or hands-free interaction approaches have involved speech and computer vision. While improving, these technologies still have drawbacks. They can be unreliable, sensitive to interference from environmental noise, and can require a person to make obtrusive motions or sounds.
Another approach is to infer user input from sensed human muscle activity. Advances in muscular sensing and processing technologies make it possible for humans to interface with computers directly with muscle activity. One sensing technology, electromyography (EMG), measures electrical potentials generated by the activity of muscle cells. EMG-based systems may use sensors that are carefully placed according to detailed knowledge of the human physiology. Specific muscle activity is measured and used to infer movements, intended or not. While this has been done with meticulously placed EMG sensors under artificial test conditions and finely tuned signal processing, to date, gestures have not been decoded from forearm EMG signals in a way that would allow everyday use.
Techniques described below relate to robust recognition of rich sets of gestures from forearm EMG signals in ways that may allow EMG sensors to be arbitrarily placed or worn.